System and method for sleep disorder diagnosis and treatment

ABSTRACT

A system and method for sleep disorder diagnosis and treatment are disclosed. A particular embodiment includes: establishing a wireless data communication interface between a networked server and a sleep metering system worn by a user, the sleep metering system including a sensor array, wireless transceiver, and a processor; activating the sleep metering system to begin collection of sensor data from the user based on data signals from the sensor array of the sleep metering system; receiving a respiratory waveform and data corresponding to a level of arterial oxygen saturation (SpO2) in the user&#39;s blood over time as an SpO2 waveform based on the collected sensor data; receiving a set of user-configured control variables; and generating a sleep efficiency score based on the respiratory waveform, the SpO2 waveform, and the user-configured control variables, the sleep efficiency score including a log of the user&#39;s respiratory effort reduction events (RERE) and respiratory effort exaggeration events.

PRIORITY PATENT APPLICATIONS

This is a continuation patent application drawing priority from U.S.patent application Ser. No. 15/413,130; filed Jan. 23, 2017; which is acontinuation-in-part patent application drawing priority from U.S.patent application Ser. No. 15/244,999; filed Aug. 23, 2016; which is acontinuation-in-part patent application drawing priority from U.S.patent application Ser. No. 14/020,741; filed Sep. 6, 2013. This presentpatent application claims priority to the referenced patentapplications. The entire disclosure of the referenced patentapplications is considered part of the disclosure of the presentapplication and is hereby incorporated by reference herein in itsentirety.

TECHNICAL FIELD

This patent application relates to computer-implemented software andnetworked systems, according to one embodiment, and more specifically toa system and method for sleep disorder diagnosis and treatment.

COPYRIGHT

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction of the patent document or thepatent disclosure, as it appears in the Patent and Trademark Officepatent files or records, but otherwise reserves all copyright rightswhatsoever. The following notice applies to the software and data asdescribed below and in the drawings that form a part of this document:Copyright 2012-2019 Somnology, Inc., All Rights Reserved.

BACKGROUND

Sleep disorders are extremely common in the general population, withestimates of 15 million adults in the U.S. with persistent insomnia, andanother 24 million adults and children suffering from obstructive sleepapnea. The current model of care is for people to seek consultation withtheir primary care doctor first about their sleep issues. From there,people are referred for sleep studies, sleep specialists, or givenmedications. Treatment is often delayed, involves potentiallyhabit-forming if medications, or is too expensive to pursue.

Smartphones are becoming the predominant link between data and people.Most current smartphones provide a capability to use mobile softwareapplications (apps). A mobile software application (app) can embody adefined set of functionality and can be installed and executed on amobile device, such as a smartphone, a tablet device, laptop computer,or other form of mobile computing or communications device. Conventionalmobile apps are available that focus on one narrow aspect or another ofdifferent sleep problems. However, these conventional mobile apps do notintegrate a set of diagnostic and therapeutic tools to fully assess andtreat a user's sleep disorder. For example, current systems do notintegrate a user's sleep history in a diagnostic and treatment system.Additionally, one may find a mobile app that plays soothing music, oranother that wakes the user up at a particular time, or another thatrecords snoring. However, these systems cannot combine data obtained ina diagnostic phase for use in a sleep disorder treatment phase.

SUMMARY

The various embodiments described herein offer a solution to theproblems identified above by providing an interactive interview with theuser, then guiding users to possible sleep disorder diagnoses andtreatments. If the user shows signs of insomnia, the app instructs theuser in behavioral therapy, provides a sleep log tool to monitorprogress, and gives feedback to the user about their progress, followingestablished clinical guidelines for treatment. If the user shows signsof obstructive sleep apnea, the user may employ the breathingdisturbance monitoring device described herein to measure respiratorypatterns at night. Screening studies for sleep apnea are integratedwithin the app itself All the tools contained in the various embodimentsdescribed herein educate the user about their sleep problem and provideimmediate assistance—with ease of use and for a low cost.

The various embodiments described herein provide a software applicationthat includes among the following features:

-   -   a. An automated and prompted sleep interview similar to an        interview a patient might receive in a doctor's office.    -   b. Feedback to the user based on the user's answers in the        prompted sleep interview.    -   c. Guidance through behavioral treatment programs, including        Stimulus Control Therapy and Sleep Restriction Therapy. The        treatment programs for a particular user are customized and        based on information gathered from the user and diagnoses        automatically determined from the prompted sleep interview.    -   d. A sleep log meter so the user can keep track of sleep        patterns in quantitative way.    -   e. Soundscapes to assist the user to achieve a relaxed state        before bed and whenever the user cannot fall asleep.    -   f. Automated information presentations that describe possible        sleep diagnoses in more detail, typical diagnostic tests,        various treatment options, and the five closest sleep centers to        the user's location.    -   g. Rewards Center where the user can redeem points collected for        keeping sleep logs or for completing other actions to which        incentives are tied. The user can redeem points for prizes, such        as animal dream totems, exclusive musical recordings, additional        soundscapes, and the like.    -   h. Breathing Disturbance Meter integration, which measures        respiratory breathing patterns at night to screen for possible        obstructive sleep apnea.    -   i. All graphs and questionnaires may be exported as PDF files        and emailed to the user and/or a doctor.    -   j. The diagnostic and treatment features of the various        embodiments were designed by a board certified sleep specialist.

The combinations of features described and claimed herein do not existin an integrated and coordinated manner in any other app or softwareproduct with a single focus—to help more people at the lowest costpossible to sleep better and therefore perform better.

BRIEF DESCRIPTION OF THE DRAWINGS

The various embodiments are illustrated by way of example, and not byway of limitation, in the figures of the accompanying drawings in which:

FIG. 1 illustrates an example embodiment of a sleep disorder diagnosisand treatment system as a hosted application;

FIG. 2 illustrates an alternative example embodiment of a sleep disorderdiagnosis and treatment system as a downloaded application;

FIG. 3 illustrates an example embodiment, implemented as a mobile deviceapplication (mobile app), that shows a home display screen of the userinterface of the example embodiment;

FIG. 4 illustrates an example embodiment, implemented as a mobile app,which shows an initial questionnaire display screen of the userinterface of the example embodiment;

FIGS. 5 through 8 illustrate an example embodiment, implemented as amobile app, which shows various questionnaire display screens of theuser interface of the example embodiment;

FIG. 9 illustrates an example embodiment, implemented as a mobile app,which shows a stimulus control therapy display screen of the userinterface of the example embodiment;

FIG. 10 illustrates an example embodiment, implemented as a mobile app,which shows a sleep restriction therapy display screen of the userinterface of the example embodiment;

FIGS. 11 and 12 illustrate an example embodiment, implemented as amobile app, which shows sleep log display screens of the user interfaceof the example embodiment;

FIG. 13 illustrates an example embodiment, implemented as a mobile app,which shows a soundscape display screen of the user interface of theexample embodiment;

FIGS. 14 through 16 are processing flow charts illustrating an exampleembodiment of a method as described herein;

FIGS. 17 and 18 illustrate charts of the breathing patterns of a patientas captured by the breathing disturbance metering system of an exampleembodiment;

FIG. 19 illustrates an example embodiment of the components of thebreathing disturbance metering system of an example embodiment;

FIG. 20 illustrates the thoracic cage of a human and the relativeplacement of the breathing disturbance metering system of an exampleembodiment;

FIGS. 21 through 25 illustrate an example embodiment, implemented as amobile app, which shows various report display screens of the userinterface of the example embodiment;

FIG. 26 illustrates another example embodiment of a networked system inwhich various embodiments may operate;

FIG. 27 is a processing flow chart illustrating an example embodiment ofa method as described herein;

FIG. 28 shows a diagrammatic representation of machine in the exampleform of a computer system within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein;

FIG. 29 illustrates an example embodiment of the components of the sleepmetering system of an example embodiment;

FIGS. 30 and 31 illustrate an example embodiment of the inductive beltcomponent of the sleep metering system of an example embodiment;

FIGS. 32 and 33 illustrate an example embodiment of the flow of sensordata signals and processed sensor data from the sleep metering system toa networked server for further processing and scoring and for networkedaccess by authorized healthcare providers;

FIG. 34 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a mild Obstructive Sleep Apnea (OSA) condition in the subjectbased on the detected respiration effort exaggeration event indicated bythe airflow and effort channels;

FIG. 35 illustrates an example of scoring a combined set of detectedevents and conditions in the same sleeping subject, the sample scoringindicating a mild OSA condition in the subject based on the detectedrespiration effort exaggeration event indicated by the airflow andeffort channels, wherein the events are more frequent;

FIG. 36 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating an abnormal pattern in the subject based on the detectedabdominal channel;

FIG. 37 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a severe OSA condition in the subject based on the redundantpattern detected in all sensor channels;

FIG. 38 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a moderate OSA condition in the subject based on a lack of asignificant detected snoring condition, but detection of an irregularbreathing pattern;

FIG. 39 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a mild OSA condition in the subject based on a lack ofdetected airflow or snoring conditions, but detection of an abdominalirregular and oxygen desaturations;

FIG. 40 illustrates an example embodiment of a sleep report containing avariety of information and datasets generated using the sensor datacaptured and processed for a particular subject by the sleep meteringsystem of an example embodiment;

FIGS. 41 through 44 illustrate example embodiments of the sleep meteringsystem of an example embodiment;

FIG. 45 illustrates an example embodiment of a set of user-configurablecontrol variables that can be used to configure the sleep scoringprocess in the example embodiment;

FIG. 46 illustrates an example embodiment of the initial processing flowof the sleep scoring process of the example embodiment;

FIG. 47 illustrates an example embodiment of the respiratory/abdominaleffort waveform filtering and amplitude calculation of the sleep scoringprocess of the example embodiment;

FIG. 48 illustrates an example embodiment of the SpO2 percentage dropcalculation of the sleep scoring process of the example embodiment;

FIG. 49 illustrates an example embodiment of the RERE scoring of thesleep scoring process of the example embodiment;

FIG. 50 illustrates an example embodiment of the REEE scoring of thesleep scoring process of the example embodiment;

FIG. 51 illustrates an example embodiment of the event coalescingprocessing of the sleep scoring process of the example embodiment;

FIG. 52 illustrates an example embodiment of the BDI calculation of thesleep scoring process of the example embodiment;

FIGS. 53 and 54 illustrate an example embodiment of the EDF annotationsfile and log files created by the sleep scoring process of the exampleembodiment; and

FIG. 55 is a processing flow chart illustrating an example embodiment ofa method as described herein.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the various embodiments. It will be evident, however,to one of ordinary skill in the art that the various embodiments may bepracticed without these specific details.

In the various embodiments described herein, a system and method forsleep disorder diagnosis and treatment are disclosed. The variousembodiments provide the ability to integrate a set of diagnostic andtherapeutic tools to fully assess and treat a user's sleep disorder in aconvenient and low cost mobile platform. In various embodimentsdescribed in detail below, a software application program is used togenerate and enable the sleep disorder diagnosis and treatment systeminterface using a computer system, a web appliance, and/or a mobiledevice. As described in more detail below, the computer or computingsystem on which the described embodiments can be implemented can includepersonal computers (PCs), portable computing devices, laptops, tabletcomputers, personal digital assistants (PDAs), personal communicationdevices (e.g., cellular telephones, smartphones, or other wirelessdevices), network computers, set-top boxes, consumer electronic devices,or any other type of computing, data processing, communication,networking, or electronic system.

A mobile version of an example embodiment provides a user-friendlyinterface from which the user can easily view and interact with thevarious information presentations and prompts from a mobile device. Asdescribed in more detail below, a mobile software application (app)embodying a mobile version of an example embodiment as described hereincan be installed and executed on a mobile device, such as a smartphone,a tablet device, laptop computer, or other form of mobile computing orcommunications device. In another embodiment, a website can be used as ahosted service provider without installing any software on the mobiledevice. In this embodiment, a user can use the features described hereinjust by directing a browser to the website host through the use of thepreinstalled or installed browser. These embodiments are described inmore detail below. In an example embodiment, a splash screen appearswhenever the user opens or launches the mobile application on the mobiledevice. This splash screen displays a host logo while opening the loginscreen or the live feed.

User log-in functionality in the mobile app provides a user-friendlyuser interface in which the user provides the email address and passwordassociated with the user account. If the user does not have an account,the user can create an account from this user interface. The process ofcreating a user account is simple and only requires the user to providethe following information: name, surname, e-mail address, and password.By completing this information, the user can create an account and getaccess to customized sleep disorder diagnosis and treatment information.

Referring now to FIG. 1, in an example embodiment 100, a system andmethod for sleep disorder diagnosis and treatment are disclosed. In oneexample embodiment, an application or service, typically provided by oroperating on a host site (e.g., a website) 110, is provided to simplifyand facilitate the downloading or hosted use of the sleep disorderdiagnosis and treatment system 200 of an example embodiment. In aparticular embodiment, the sleep disorder diagnosis and treatment system200 can be downloaded from the host site 110 by a user at a userplatform 140. Alternatively, the sleep disorder diagnosis and treatmentsystem 200 can be hosted by the host site 110 for a networked user at auser platform 140. The details of the sleep disorder diagnosis andtreatment system 200 of an example embodiment are provided below.

Referring again to FIG. 1, the sleep disorder diagnosis and treatmentsystem 200 can be hosted by host site 110 and be in networkcommunication with a plurality of user platforms 140. The host site 110and user platforms 140 may communicate and transfer data and informationin the data network ecosystem 100 shown in FIG. 1 via a wide area datanetwork (e.g., the Internet) 120. Various components of the host site110 can also communicate internally via a conventional intranet or localarea network (LAN) 114.

Networks 120 and 114 are configured to couple one computing device withanother computing device. Networks 120 and 114 may be enabled to employany form of computer readable media for communicating information fromone electronic device to another. Network 120 can include the Internetin addition to LAN 114, wide area networks (WANs), direct connections,such as through an Ethernet port or a universal serial bus (USB) port,other forms of computer-readable media, or any combination thereof. Onan interconnected set of LANs, including those based on differingarchitectures and protocols, a router and/or gateway device acts as alink between LANs, enabling messages to be sent between computingdevices. Also, communication links within LANs typically include twistedwire pair or coaxial cable, while communication links between networksmay utilize analog telephone lines, full or fractional dedicated digitallines including T1, T2, T3, and T4, Integrated Services Digital Networks(ISDNs), Digital Subscriber Lines (DSLs), wireless links includingsatellite links, or other communication links known to those of ordinaryskill in the art. Furthermore, remote computers and other relatedelectronic devices can be remotely connected to either LANs or WANs viaa wireless link, WiFi, Bluetooth, satellite, or modem and temporarytelephone link.

Networks 120 and 114 may further include any of a variety of wirelesssub-networks that may further overlay stand-alone ad-hoc networks, andthe like, to provide an infrastructure-oriented connection. Suchsub-networks may include mesh networks, Wireless LAN (WLAN) networks,cellular networks, and the like. Networks 120 and 114 may also includean autonomous system of terminals, gateways, routers, and the likeconnected by wireless radio links or wireless transceivers. Theseconnectors may be configured to move freely and randomly and organizethemselves arbitrarily, such that the topology of networks 120 and 114may change rapidly and arbitrarily.

Networks 120 and 114 may further employ a plurality of accesstechnologies including 2nd (2G), 2.5, 3rd (3G), 4th (4G) generationradio access for cellular systems, WLAN, Wireless Router (WR) mesh, andthe like. Access technologies such as 2G, 3G, 4G, and future accessnetworks may enable wide area coverage for mobile devices, such as oneor more of client devices 141, with various degrees of mobility. Forexample, networks 120 and 114 may enable a radio connection through aradio network access such as Global System for Mobile communication(GSM), General Packet Radio Services (GPRS), Enhanced Data GSMEnvironment (EDGE), Wideband Code Division Multiple Access (WCDMA),CDMA2000, and the like. Networks 120 and 114 may also be constructed foruse with various other wired and wireless communication protocols,including TCP/IP, UDP, SIP, SMS, RTP, WAP, CDMA, TDMA, EDGE, UMTS, GPRS,GSM, UWB, WiFi, WiMax, IEEE 802.11x, and the like. In essence, networks120 and 114 may include virtually any wired and/or wirelesscommunication mechanisms by which information may travel between onecomputing device and another computing device, network, and the like. Inone embodiment, network 114 may represent a LAN that is configuredbehind a firewall (not shown), within a business data center, forexample.

The sleep disorder diagnosis and treatment system can be implementedusing any form of network transportable digital data. The networktransportable digital data can be transported in any of a group of fileformats, protocols, and associated mechanisms usable to enable a hostsite 110 and a user platform 140 to transfer data over a network 120. Inone embodiment, the data format for the user interface can be HyperTextMarkup Language (HTML). HTML is a common markup language for creatingweb pages and other information that can be displayed in a web browser.In another embodiment, the data format for the user interface can beExtensible Markup Language (XML). XML is a markup language that definesa set of rules for encoding interfaces or documents in a format that isboth human-readable and machine-readable. In another embodiment, a JSON(JavaScript Object Notation) format can be used to stream the interfacecontent to the various user platform 140 devices. JSON is a text-basedopen standard designed for human-readable data interchange. The JSONformat is often used for serializing and transmitting structured dataover a network connection. JSON can be used in an embodiment to transmitdata between a server, device, or application, wherein JSON serves as analternative to XML.

In a particular embodiment, a user platform 140 with one or more clientdevices 141 enables a user to access data and provide data for the sleepdisorder diagnosis and treatment system 200 via the host 110 and network120. Client devices 141 may include virtually any computing device thatis configured to send and receive information over a network, such asnetwork 120. Such client devices 141 may include portable devices 144,such as, cellular telephones, smartphones, display pagers, radiofrequency (RF) devices, infrared (IR) devices, global positioningdevices (GPS), Personal Digital Assistants (PDAs), handheld computers,wearable computers, tablet computers, integrated devices combining oneor more of the preceding devices, and the like. Client devices 141 mayalso include other computing devices, such as personal computers 142,multiprocessor systems, microprocessor-based or programmable consumerelectronics, network PC's, and the like. Client devices 141 may alsoinclude other processing devices, such as consumer electronic (CE)devices 146 and/or mobile computing devices 148, which are known tothose of ordinary skill in the art. As such, client devices 141 mayrange widely in terms of capabilities and features. For example, aclient device configured as a cell phone may have a numeric keypad and afew lines of monochrome LCD display on which only text may be displayed.In another example, a web-enabled client device may have a touchsensitive screen, a stylus, and many lines of color LCD display in whichboth text and graphics may be displayed. Moreover, the web-enabledclient device may include a browser application enabled to receive andto send wireless application protocol messages (WAP), and/or wiredapplication messages, and the like. In one embodiment, the browserapplication is enabled to employ HyperText Markup Language (HTML),Dynamic HTML, Handheld Device Markup Language (HDML), Wireless MarkupLanguage (WML), WMLScript, JavaScript™, EXtensible HTML (xHTML), CompactHTML (CHTML), and the like, to display and/or send digital information.In other embodiments, mobile devices can be configured with applications(apps) with which the functionality described herein can be implemented.

Client devices 141 may also include at least one client application thatis configured to send and receive content data or/or control data fromanother computing device via a wired or wireless network transmission.The client application may include a capability to provide and receivetextual data, graphical data, video data, audio data, and the like.Moreover, client devices 141 may be further configured to communicateand/or receive a message, such as through an email application, a ShortMessage Service (SMS), direct messaging (e.g., Twitter™), MultimediaMessage Service (MMS), instant messaging (IM), internet relay chat(IRC), mIRC, Jabber, Enhanced Messaging Service (EMS), text messaging,Smart Messaging, Over the Air (OTA) messaging, or the like, betweenanother computing device, and the like.

As one option, the sleep disorder diagnosis and treatment system 200, ora portion thereof, can be downloaded to a user device 141 of userplatform 140 and executed locally on a user device 141 (e.g., see FIG. 2described below). The downloading of the sleep disorder diagnosis andtreatment system 200 application (or a portion thereof) can beaccomplished using conventional software downloading functionality. As asecond option, the sleep disorder diagnosis and treatment system 200 canbe hosted by the host site 110 and executed remotely, from the user'sperspective, on host system 110. In one embodiment, the sleep disorderdiagnosis and treatment system 200 can be implemented as a service in aservice-oriented architecture (SOA) or in a Software-as-a-Service (SAAS)architecture. In any case, the functionality performed by the sleepdisorder diagnosis and treatment system 200 is as described herein,whether the application is executed locally or remotely, relative to theuser.

Referring again to FIG. 1, the sleep disorder diagnosis and treatmentsystem 200 of an example embodiment is shown to include a sleep disorderdiagnosis and treatment system database 103. The database 103 is used inan example embodiment for data storage of information related to thesleep disorder diagnosis and treatment of users, the sleep logs ofusers, communications between users and host representatives, shareddocuments, images, soundscapes, metadata, and the control data formanaging the user interactions and the associated user interfaces. Itwill be apparent to those of ordinary skill in the art that the database103 can be used for the storage of a variety of data in support of thesleep disorder diagnosis and treatment system 200 of an exampleembodiment.

Referring to FIG. 2, an alternative embodiment 101 is shown in which thesleep disorder diagnosis and treatment system 200 and associateddatabase 103 can be downloaded and executed locally at a user device 141of user platform 140. In other embodiments, portions of system 200and/or database 103 can be executed locally at a user device 141 of userplatform 140 or executed remotely at the host site 110. In each of theseimplementations, the functionality and structure of the sleep disorderdiagnosis and treatment system 200 of an example embodiment is similarto the example embodiments described herein.

Referring again to FIGS. 1 and 2, an example embodiment is shown toinclude a sleep disorder diagnosis and treatment system 200. Sleepdisorder diagnosis and treatment system 200 can include a sleep disorderdiagnostic module 210, a sleep disorder treatment module 220, aBreathing Disturbance Meter integration module 230, a reports module240, a user account management module 250, and an administrativemanagement module 260. Each of these modules can be implemented assoftware components executing within an executable environment of sleepdisorder diagnosis and treatment system 200 operating on host site 110or user platform 140. Each of these modules of an example embodiment isdescribed in more detail below in connection with the figures providedherein.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is shown to include asleep disorder diagnostic module 210. The sleep disorder diagnosticmodule 210 can be used to query information from a user and to use theinformation to make a sleep disorder diagnosis. In particular, the sleepdisorder diagnostic module 210 can be used to present, prompt, andprocess user input on various user questionnaires. The userquestionnaires include sleep-related interview questions and a STOPBANGquestionnaire. The STOPBANG questionnaire screens for signs ofObstructive Sleep Apnea (OSA). STOPBANG is an acronym representing thesymptoms that can be indicative of OSA: Snoring, Feeling Tired, Observedpauses in breathing, High blood Pressure, Body mass index greater than35, Age greater than 50, Neck circumference greater than 40 cm (16 in),Male Gender, or post-menopausal female. Three or more positive answersfrom the user on the STOPBANG questionnaire are considered potentiallyindicative of OSA.

The sleep disorder diagnostic module 210 can be configured to generate auser interface to prompt a user for input on various questionnairesrelated to the user's sleep patterns. Based on the user's input, thesleep disorder diagnostic module 210 can determine whether or not theuser is showing signs or symptoms of common sleep disorders. Thesequestionnaires are not designed to provide a certain diagnosis. Rather,the sleep disorder diagnostic module 210 can process the user responsesand automatically determine if the user is showing signs that suggest apossible sleep disorder or a possible sleep-related breathing problem.In this case, the sleep disorder diagnostic module 210 can signal thesleep disorder treatment module 220 to suggest particular sleep disordertreatments or OSA-related treatments. The sleep disorder diagnosticmodule 210 can be configured to store the user's responses to enable theuser to share the responses later with a health care provider. The sleepdisorder diagnostic module 210 can also be configured to use thesequestionnaires and related responses to start a discussion with theuser's health care provider on associated sleep issues to determinewhether a particular treatment is needed. If treatment is indicated, thesleep disorder treatment module 220 can be configured to recommend atype of treatment.

Referring now to FIG. 3, an example embodiment, implemented as a mobiledevice application (mobile app), shows a splash screen or home displayscreen of the user interface of the example embodiment. The home displayscreen is displayed on a user's mobile device when the mobile app islaunched. FIG. 4 illustrates an example embodiment, implemented as amobile app, which shows an initial questionnaire display screen of theuser interface of the example embodiment. The initial questionnairedisplay screen can be presented when the user activates a particularicon presented in the home display screen.

FIGS. 5 through 8 illustrate an example embodiment, implemented as amobile app, which shows various questionnaire display screens of theuser interface of the example embodiment. The series of questionnairespresented by the sleep disorder diagnostic module 210 prompt the appuser for basic demographic information, such as age and date of birth.The various questionnaires also contain questions related to the user'ssleep habits, such as the user's typical bedtime and whether the usertakes any medications before bed. These are important questions that canaffect the quality of sleep. The sleep interview presented and managedby the sleep disorder diagnostic module 210 serves as a starting pointfor a discussion and potential diagnosis of the user's sleep patterns.Based on the user's input, the sleep disorder diagnostic module 210 candetermine whether or not the user is showing signs or symptoms of commonsleep disorders. The sleep disorder diagnostic module 210 can signal thesleep disorder treatment module 220 to suggest to the user and/or selectfor the user particular sleep disorder treatments or OSA-relatedtreatments.

Referring to FIG. 8, the sleep disorder diagnostic module 210 can alsobe used to present, prompt, and process user input on an EpworthSleepiness Scale. The Epworth Sleepiness Scale (ESS) is a scale intendedto assess daytime sleepiness that is measured by use of a very shortquestionnaire. The ESS measures how likely the user is to fall asleep asrated on a scale from 0 to 3, with 0 being not at all likely and 3 beingvery likely. A cumulative score of ten or higher on this scale suggeststhat the user is abnormally sleepy, and may point to an underlying sleepdisorder. This can be helpful in diagnosing sleep disorders. The ESS wasintroduced in 1991 by Dr. Murray Johns of Epworth Hospital in Melbourne,Australia.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is shown to include asleep disorder treatment module 220. The sleep disorder treatment module220 can be used to suggest to the user and/or select for the userparticular sleep disorder treatments or OSA-related treatments based onthe user responses captured and processed by the sleep disorderdiagnostic module 210. In an example embodiment, these sleep disordertherapies or treatments can include: 1) behavioral therapies, 2)stimulus control therapy (SCT), 3) sleep restriction therapy (SRT),sleep logs, and sleep efficiency assessments, and 4) soundscapes. Thesevarious therapies as implemented by the sleep disorder treatment module220 of an example embodiment are described in more detail below.

Behavioral therapies are treatments that focus on changing a person'sbehavior in order to treat a condition. The goal of behavioral therapyis to reinforce beneficial behaviors and discourage behaviors that areharmful or unhelpful. Behavioral therapies encourage the user to createhealthy habits and to break unhealthy ones.

Behavioral therapy for insomnia may include a cognitive component, andthus is termed Cognitive Behavioral Therapy, or CBT. CBT is designed tochange behaviors by focusing on how people think and feel about theirbehaviors and their disorder. CBT is meant to address negative emotions,such as anxiety or depression that may be associated with having adisorder. CBT also explores the thought processes that people go throughwhile engaging in unhealthy behaviors and habits, which may indicate whythey engage in those behaviors. The goal of CBT is to address the rootcauses of unhealthy behaviors and habits (i.e., negative emotions andthought processes), so that these behaviors can be replaced byhealthier, more positive ones. The sleep disorder treatment module 220can be used to guide the user through various CBT treatments based onthe assessment of their condition as determined by the sleep disorderdiagnostic module 210. The example embodiment focuses on behavioraltherapies because these therapies are safer and longer-lasting thansleep medications, and may be started or re-started at any time. Incontrast to some types of sleeping pills or other medications,behavioral therapy does not result in a “tolerance” that limitscontinued benefit. The sleep disorder treatment module 220 recognizesthat changing established behaviors is very difficult. That's why theapp provides positive feedback to encourage users to keep trying. Thesleep disorder treatment module 220 includes several therapies thatteach users to help themselves sleep better. The sleep disordertreatment module 220 also presents, prompts, and retains sleep logs sousers can keep track of their sleep disorder treatment progress. Thesleep logs and the related reports are generated by the sleep disordertreatment module 220 and the reports module 240. The goal of the variousembodiments and treatment strategies is to help increase the quality ofsleep in meaningful and lasting ways.

FIG. 9 illustrates an example embodiment, implemented as a mobile app,which shows a stimulus control therapy display screen of the userinterface of the example embodiment. As part of the treatment or therapyoptions provided by the sleep disorder treatment module 220, a StimulusControl Therapy, or SCT, is used to treat insomnia. In the exampleembodiment, there are five main principles of SCT:

-   -   a. Go to bed when sleepy, not when you think you should go to        bed.    -   b. Get out of bed when not sleeping. Do not stay in bed for more        than 15-20 minutes, even if you are actively trying to sleep.    -   c. No naps.    -   d. Use bed and bedroom for sleep (or sexual intimacy) only.    -   e. Maintain a regular wake-up/get-up time (on weekdays,        weekends, and holidays).

One of the goals of SCT is to help the user associate the bed andbedroom with sleep rather than the inability to sleep. Another goal isto help the user break habits that keep the vicious cycle of insomniagoing, such as staying in bed half of the day to try to “catch up” onlost sleep. SCT helps the user to create new, healthy habits, andencourages the user to maintain a regular sleep/wake cycle. As part ofthis encouragement, the sleep disorder treatment module 220 guides theuser through a set of display screens, information presentations, andprompts. For example, FIG. 9 illustrates how the user can transitionfrom the home display screen to a set of SCT display screens. The SCTdisplay screen section allows the user to review the SCT guidelines andreminds the user of the guidelines before bedtime. The user is promptedto create, follow, and assess their sleep logs using SCT to determine iftheir sleep problems can be resolved with this treatment intervention.In a particular embodiment, the sleep logs can be accessed through thereports page.

FIG. 10 illustrates an example embodiment, implemented as a mobile app,which shows a sleep restriction therapy display screen of the userinterface of the example embodiment. As part of the treatment or therapyoptions provided by the sleep disorder treatment module 220, a nextlevel of behavioral therapy for insomnia is Sleep Restriction Therapy(SRT). SRT is a treatment program that uses controlled sleep deprivationas a therapy. Sleep deprivation is used as a tool to help the user fallasleep and stay asleep. In a typical treatment scenario as guided by thesleep disorder treatment module 220, the user can begin by choosing anSRT program with seven hours of sleep a night or an SRT program with sixhours of sleep a night. If the user has already started SRT and is inthe middle of an SRT monitoring period, the user cannot start a newprogram. It will be apparent to those of ordinary skill in the art thatSRT programs with other sleep time periods (e.g., other than six orseven hours of sleep a night) can also be implemented. As part of theSRT program, the user is prompted to set their desired wake-up time asguided by the sleep disorder treatment module 220. Then, the sleepdisorder treatment module 220 can assign a bedtime for the user that issix or seven hours prior to the desired wake-up time (depending on theSRT program time period selected by the user). The sleep disordertreatment module 220 can remind the user to wind down an hour beforeassigned bedtime, so that the user will be calm and not over-stimulatedwhen they try to go to sleep.

FIGS. 11 and 12 illustrate an example embodiment, implemented as amobile app, which shows sleep log display screens of the user interfaceof the example embodiment. As guided by the sleep disorder treatmentmodule 220, the user can enter their sleep log information by clickingon the right upper corner of a reports page screen presented by thereports module 240. Each morning, the user can use the sleep log toindicate when they got into bed and when they got out of bed, and whenthey actually fell asleep and when they woke up. As shown in FIG. 11,the user can make these entries in the sleep log by sliding the iconsalong the bars as shown. Once the user enters this information, thesleep disorder treatment module 220 can highlight in dark blue the hoursthe user was in the bed as shown in FIG. 11. If the user experiencedawakenings in the middle of the night, the user may tap on each of thehighlighted hour-long units individually to add details, such as theexact time the user woke up, how long the user was awake, and whatoccurred during each awakening as shown in FIG. 12. Using all theinformation entered into the sleep log by the user, the sleep disordertreatment module 220 can calculate the user's sleep efficiency. In anexample embodiment, the sleep efficiency can be calculated as thepercentage of time the user was in bed relative to the time spent by theuser actually sleeping. The goal for nominal sleep efficiency is apercentage equal to or greater than 85%.

Although improvement in sleep quality is the ultimate goal for mostpeople, improvement in sleep efficiency is a primary goal. Sleepefficiency is the sleep feature directly changeable by changing aperson's behavior. It turns out that for most people, improvement insleep quality follows improvement in sleep efficiency. If this is notthe case, then another sleep problem may be present, like OSA or anotherdiagnosis.

Once the user achieves a consistent 85% sleep efficiency for two weeksas guided and monitored by the sleep disorder treatment module 220, theuser is prompted to lengthen their sleep period gradually. In support ofthis feature, the sleep disorder treatment module 220 can automaticallyprompt the user to go to bed 15 minutes earlier every two weeks. The SRTtreatment is complete once the user reaches and maintains an 85% sleepefficiency and feels generally refreshed during the day. The user willhave then learned how much sleep they need to feel refreshed. Noteveryone needs the mythic “eight hours of sleep per night.” Some peopleneed more sleep and others need less sleep. The sleep disorder treatmentmodule 220 is designed to accommodate a variety of sleep patterns in avariety of different users.

The sleep disorder treatment module 220 is designed to be used as asleep maintenance tool as well, even after improvement in sleep patternsis achieved. The sleep disorder treatment module 220 can prompt users tokeep daily sleep logs. If the user's sleep efficiency begins to fall,the sleep disorder treatment module 220 can offer another round of SRTor other therapy, starting from the beginning. Also, if a seven hoursleep restriction period does not improve the user's sleep efficiency,the user can be offered a six hour sleep restriction period.

It is important to note that Sleep Restriction Therapy may make the userfeel worse before feeling better. This is because the SRT treatmentremoves the current “sleep coping mechanism” being used, and replaces itwith a specified sleep schedule purposely meant to initially cause sleepdeprivation. If the user sticks with the SRT treatment program, thesleep deprivation lessens and the user will start sleeping and feelingbetter. In a particular embodiment, a Rewards Center is provided wherethe user can redeem points collected for keeping sleep logs or forcompleting other actions to which incentives are tied. The user canredeem points for prizes, such as animal dream totems, exclusive musicalrecordings, additional soundscapes, and the like. Additionally, anexample embodiment can provide automated information presentations thatdescribe possible sleep diagnoses in more detail, typical diagnostictests, various treatment options, and the five closest sleep centers tothe user's location.

FIG. 13 illustrates an example embodiment, implemented as a mobile app,which shows a soundscape display screen of the user interface of theexample embodiment. The soundscape display screen offers the user avariety of selectable soundscapes that can be played when the user goesto sleep or awakens. The soundscapes are audio clips of calming soundsand relaxing music, as well as meditations to help the user relax at thebeginning or end of their day.

FIGS. 14 through 16 are processing flow charts illustrating an exampleembodiment of a method as described herein. In the manner describedabove, the sleep disorder diagnostic module 210 and the sleep disordertreatment module 220 guide the user through a process of sleep disorderdiagnostic assessment and sleep disorder treatment options customizedfor the particular user. As a result, the user achieves improved sleepefficiency.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is shown to include abreathing disturbance meter integration module 230. The breathingdisturbance meter integration module 230 can be used to detect an OSAcondition or other sleep-related condition in a patient. The breathingdisturbance meter integration module 230 is in wireless datacommunication with a breathing disturbance metering system described inmore detail below.

Because the vast majority of people with sleep apnea have not yet beendiagnosed, two barriers to sleep apnea detection may be the cost andaccessibility of testing. After interpreting thousands of sleep studies,both lab and home studies, the inventor herein determined that apatient's sleep apnea can be detected the majority of the time in asingle abdominal effort channel, wherein the breathing patterns of apatient are captured by a breathing disturbance metering system.

FIGS. 17 and 18 illustrate charts of the breathing patterns of a patientas captured by a breathing disturbance metering system of an exampleembodiment. As shown in FIG. 17, a sleep apnea condition is a 90% orgreater decrease in the breathing airflow of a sleeping person lastingten seconds or longer. As shown in FIG. 18, a sleep hypopnea conditionis a 30% or greater decrease in airflow (from an established baseline)lasting ten seconds or longer. By monitoring the breathing patterns of asleeping person, the sleep apnea condition and sleep hypopnea conditioncan be detected. The breathing disturbance metering system and breathingdisturbance meter integration module 230 can be used to detect, measure,assess, and respond to the potential OSA conditions present in a patientbeing monitored. The details of this system in an example embodiment aredescribed below.

FIG. 19 illustrates an example embodiment of the components of thebreathing disturbance metering system 500 of an example embodiment. Asdescribed herein, the breathing disturbance metering system 500 can alsobe denoted the sleep metering system or on-body device 500. Thebreathing disturbance metering system 500 can include a chest expansionmeasuring device 510 and/or a movement detection device 520. Breathingmonitors are well-known in the art. The chest expansion measuring device510 and/or the movement detection device 520 serve to detect themovements of the chest cavity during the normal breathing cycles of aperson. Once the breathing disturbance metering system 500 is calibratedto the normal breathing patterns of a particular individual, the degreeof chest cavity movement can correlate to the volume of air respired foreach breath. As explained above, anomalies in the breathing patterns ofa particular individual can correlate to variations in the degree ofchest cavity movement in a series of breaths over a given time period.In an example embodiment, the movement detection device 520 can includea level that can measure degrees of movement up and down or side toside.

These variations in breathing patterns can be aggregated into abreathing disturbance index (BDI). Because the breathing disturbancemetering system is not measuring airflow, we derived new terminology toguide the development of a device able to measure abnormalities of one'sbreathing pattern based on abdominal movements. Respiratory effortreduction events, or RERE's, are defined as a >=30% reduction inamplitude of a preceding abdominal effort waveform, lasting 10 secondsor longer or compared to a running baseline over a pre-definedtimeframe. A respiratory effort exaggeration event (REEE) is an increasein a respiratory waveform of 150% to 400% over baseline. A REEE is notcounted if immediately adjacent to a RERE to avoid double scoring thesame event. A RERE that is 10-29% reduced from baseline but accompaniedby a >=3% fall in O² saturation is counted as an event. In general, theBDI for a particular subject can be determined from the generalequation: BDI=#RERE's+#REEE's/total recording time in hours. Segments ofdata artifacts are identified and removed from the equation.

In the example embodiment, the BDI can be computed as follows:

${BDI} = \frac{{\# {{RERE}'}s} + {\# {{REEE}'}s}}{\# {hours}\mspace{14mu} {of}\mspace{14mu} {{testing}/{recording}}\mspace{14mu} {time}}$

In an example embodiment, the breathing disturbance metering system 500can use clock 540 to measure a start and stop time. Initially, the usercan press a button on the device 500 to activate the device 500 whenthey retire to the bed. In the morning, the user can press a button todeactivate the device 500 after they wake up. After activation, thebreathing disturbance metering system 500 can sample the user'sbreathing patterns over pre-configured time periods (sampling period).For example, the breathing disturbance metering system 500 can beconfigured to sample the user's chest cavity movement (i.e., breathingpattern) for a five minute sampling period. During this sampling timeperiod, the breathing disturbance metering system 500 can record theuser's breathing patterns and save this data in the memory 560. At theend of the five minute sampling period, the breathing disturbancemetering system 500 can enter a low power mode (rest period) to savepower. At a configurable time later (e.g., 15 minutes), the breathingdisturbance metering system 500 can start a new five minute samplingperiod during which a new set of breathing pattern data is gathered andretained in memory 560. Each set of breathing pattern data is timestamped with the current or relative time/date. In this manner, thebreathing disturbance metering system 500 can gather breathing patterndata for the user over the entire night. As the breathing pattern datafor a particular user is gathered, the breathing disturbance meteringsystem 500 can use the processor 550 to scan the data for patternsconsistent with apnea or hypopnea conditions. The processor 550 canscore any detected respiratory effort reduction or exaggerationcondition (e.g., RERE or REEE) for a level of severity. The respiratoryeffort detection data and related scoring can also be stored in thememory 560. It will be apparent to those of ordinary skill in the artthat a variety of related data can also be generated and retained. Itwill also be apparent to those of ordinary skill in the art thatdifferent sampling periods and/or rest periods can be used for aparticular embodiment. Periodically throughout the night or thefollowing morning, the breathing disturbance metering system 500 canestablish wireless data communications with the breathing disturbancemeter integration module 230 and upload the data saved in memory 560 toa memory in the user platform 140. A wireless transceiver and mobiledevice interface 570 is provided in the breathing disturbance meteringsystem 500 to enable this data communication. An energy storage device(e.g., a battery) 580 is provided in the breathing disturbance meteringsystem 500 to power the system. In a particular embodiment, a soundcapture device (e.g., a microphone) 530 can be used to record the soundsmade by the user during a sleep cycle. These sounds can include snoring,which can also be an indicator disrupted sleep. For example, a decibelvariation in a periodic manner can be consistent with sleep disorderedbreathing. In this manner, the sleep disorder diagnosis and treatmentsystem 200 of an example embodiment can obtain real-time data related tothe sleeping patterns of a user and data related to potential OSAconditions present in a patient being monitored.

FIG. 20 illustrates the thoracic cage of a human and the relativeplacement of the breathing disturbance metering system 500 of an exampleembodiment. As shown in FIG. 20, the breathing disturbance meteringsystem 500 can be positioned for data recording at the top of the user'sabdomen with the top of the device positioned at the bottom of theuser's sternum. The breathing disturbance metering system 500 can beheld in place with a belt or straps 502.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is shown to include areports module 240. The reports module 240 can be used to presentinformation to a user in the form of tabular or graphical reports. FIGS.21 through 25 illustrate an example embodiment, implemented as a mobileapp, which shows various report display screens of the user interface ofthe example embodiment. In support of a variety of tabular reports, thereports module 240 can present a Sleep Log Report showing a list ofnightly summaries of information from the user's sleep logs, such as thetime the user went to sleep, total number of awakenings during thenight, and how long the user slept. The Activity Report shows a list ofnightly details about nighttime awakenings, such as why the user woke upand how long the user was awake.

In support of a variety of graphical reports, the reports module 240 canpresent several report formats in the form of graphs. For example, theuser can look at the number of awakenings and sleep efficiency on anightly basis. The user can also look at the average number ofawakenings and average sleep efficiency over a specified period of time.This allows the user to look at detailed nightly data, as well astrends, and determine whether or not progress is being made in a sleepdisorder treatment program.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is also shown to include auser account management module 250. The user account management module250 can be used to create and maintain a user account with the sleepdisorder diagnosis and treatment system 200. The user account managementmodule 250 can also be used to configure user settings, create andmaintain a user/user profile, and otherwise manage user data andoperational parameters for the sleep disorder diagnosis and treatmentsystem 200. In the example embodiment described herein, a user canregister as an identified and authenticated user in order to interactwith system 200 and receive sleep disorder diagnosis and treatment. Theregistered user can enter their name, email address, and other personalinformation. Once this information is entered, a user account is createdand the user can interact with system 200 and receive sleep disorderdiagnosis and treatment.

Referring again to FIGS. 1 and 2, the sleep disorder diagnosis andtreatment system 200 of an example embodiment is shown to include anadministrative management module 260. The administrative managementmodule 260 can be used by an agent of the sleep disorder diagnosis andtreatment system 200 to manage user accounts and to manage the sleepdisorder diagnosis and treatment system. The administrative managementmodule 260 can also be used to enforce privacy protections and contentcontrols for users. Moreover, the administrative management module 260can also be used to generate and/or process a variety of analyticsassociated with the operation of the sleep disorder diagnosis andtreatment system 200. For example, the administrative management module260 can generate various statistical models that represent the activityof the community of users and related diagnoses and treatments. Theseanalytics can be shared, licensed, or sold to others under strictprivacy protections and/or as data that has been processed to removepersonal user information.

Although the various user interface displays provided by the exampleembodiments described herein are nearly infinitely varied, severalsample user interface displays and sequences are provided herein and inthe corresponding figures to describe various features of the disclosedembodiments. These sample user interface displays and sequences aredescribed herein and in the accompanying figures. It will be apparent tothose of ordinary skill in the art that equivalent user interfacedisplays and sequences can be implemented within the scope of theinventive subject matter disclosed and claimed herein.

Referring now to FIG. 26, another example embodiment 102 of a networkedsystem in which various embodiments may operate is illustrated. In theembodiment illustrated, the host site 110 is shown to include the sleepdisorder diagnosis and treatment system 200. The sleep disorderdiagnosis and treatment system 200 is shown to include the functionalcomponents 210 through 260, as described above. In a particularembodiment, the host site 110 may also include a web server 404, havinga web interface with which users may interact with the host site 110 viaa user interface or web interface. The host site 110 may also include anapplication programming interface (API) 402 with which the host site 110may interact with other network entities on a programmatic or automateddata transfer level. The API 402 and web interface 404 may be configuredto interact with the sleep disorder diagnosis and treatment system 200either directly or via an interface 406. The sleep disorder diagnosisand treatment system 200 may be configured to access a data storagedevice 103 and data 408 therein either directly or via the interface406.

Referring now to FIG. 27, a processing flow diagram illustrates anexample embodiment of a sleep disorder diagnosis and treatment system200 as described herein. The system 800 of an example embodiment isconfigured to: generate a first user interface for sleep disorderdiagnosis, the first user interface including a plurality ofquestionnaires for prompting user input on a plurality of queriesrelated to a user's sleep patterns, at least one questionnaire includingqueries related to the user's potential Obstructive Sleep Apnea (OSA)condition (processing block 810); generate a second user interface forsleep disorder treatment, the second user interface including aplurality of sleep disorder therapies to guide the user through aparticular sleep disorder therapy automatically selected for the userbased on the user's responses captured and processed by the first userinterface, the second user interface being further configured to captureand retain a sleep log, the second user interface being furtherconfigured to generate a sleep efficiency assessment (processing block820); and detect an OSA condition in the user by use of a breathingdisturbance metering system in wireless data communication with thefirst user interface and the second user interface (processing block830).

FIG. 28 shows a diagrammatic representation of a machine in the exampleform of a computer system 700 within which a set of instructions whenexecuted may cause the machine to perform any one or more of themethodologies discussed herein. In alternative embodiments, the machineoperates as a standalone device or may be connected (e.g., networked) toother machines. In a networked deployment, the machine may operate inthe capacity of a server or a client machine in server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine may be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” can alsobe taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 700 includes a data processor 702 (e.g., acentral processing unit (CPU), a graphics processing unit (GPU), orboth), a main memory 704 and a static memory 706, which communicate witheach other via a bus 708. The computer system 700 may further include adisplay unit 710 (e.g., a liquid crystal display (LCD), plasma, or acathode ray tube (CRT)). The computer system 700 also includes an inputdevice 712 (e.g., a keyboard), a cursor control device 714 (e.g., acomputer mouse or trackpad), a disk drive unit 716, a signal generationdevice 718, and a network interface device 720.

The disk drive unit 716 includes a machine-readable medium 722 on whichis stored one or more sets of instructions (e.g., software 724)embodying any one or more of the methodologies or functions describedherein. The instructions 724 may also reside, completely or at leastpartially, within the main memory 704, the static memory 706, and/orwithin the processor 702 during execution thereof by the computer system700. The main memory 704 and the processor 702 also may constitutemachine-readable media. The instructions 724 may further be transmittedor received over a network 726 via the network interface device 720.While the machine-readable medium 722 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single non-transitory medium or multiplenon-transitory media (e.g., a centralized or distributed database,and/or associated caches and servers) that store the one or more sets ofinstructions. The term “machine-readable medium” can also be taken toinclude any non-transitory medium that is capable of storing, encodingor carrying a set of instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thevarious embodiments, or that is capable of storing, encoding or carryingdata structures utilized by or associated with such a set ofinstructions. The term “machine-readable medium” can accordingly betaken to include, but not be limited to, solid-state memories, opticalmedia, and magnetic media.

FIG. 29 illustrates an example embodiment of the components of the sleepmetering system 500 of an example embodiment. As shown, an exampleembodiment can include a housing 505 to contain an array of sensors fordetecting various conditions and events in a human subject wearing thesleep metering system 500 around their chest. The housing 505 isconfigured in an elongated low profile design to minimize the discomfortof a subject wearing the sleep metering system 500 during sleep. A beltor strap 510 is coupled to the housing 505 in a loop to enable the sleepmetering system 500 to be attached to the chest of a subject while thesubject is sleeping. The housing 505 of the sleep metering system 500 isconfigured to include an array of sensors including: an accelerometer,gyroscope, or other motion detection device 520 to detect and measuremovement and body position of the subject wearing the sleep meteringsystem 500; and a microphone or other sound detection device to detectand measure a sound level or snoring event of the subject wearing thesleep metering system 500. The housing 505 of the sleep metering system500 is also configured with an electrical connector 507 to provide anelectrical/data interface with additional sensors of the array ofsensors. In an example embodiment, the additional sensors coupled to thehousing 505 via connector 507 can include: a pulse oximeter device 532to detect and measure a level of arterial oxygen saturation (SpO2) inthe blood and the heart rate of the subject wearing the sleep meteringsystem 500; and other biomedical devices 534 to detect and measure otherconditions and events (e.g., temperature, pressure, chemical or acidityconcentration, blood viscosity, biological magnetic field, galvanic skinresponse (GSR), wireless interfaces to implanted electromechanicaldevices, pacemakers, implanted insulin pumps, and the like) in a subjectwearing the sleep metering system 500.

FIGS. 30 and 31 illustrate an example embodiment of the inductive beltcomponent of the sleep metering system 500 of an example embodiment. Theadditional sensors of the array of sensors of the sleep metering system500 can also include a chest expansion measuring device or a device tomeasure respiratory effort 510. In an example embodiment, an inductivebelt attached to the housing 505 is provided for this purpose. Therespiratory effort detection device 510 can be used to detect andmeasure airflow and respiratory effort in the subject. The raw sensordata from each of these sensors of the sensor array can be transferredto a processor 550 of the sleep metering system 500. The processor 550can pre-process the raw sensor data (e.g., filter, sequence, timestamp,normalize, packetize, etc.) for subsequent transfer to a central server110 via the wireless transceiver 570. The pre-processing performed bythe processor 550 can also include signal processing to removeundesirable noise from the raw sensor data. The pre-processing performedby the processor 550 can also include filters to reject or removeinvalid sensor data that may be received when the sleep metering system500 is detached from the subject or when the subject is awake and notready to activate the sensor data recording and processing functions ofthe sleep metering system 500.

FIGS. 32 and 33 illustrate an example embodiment of the flow ofpre-processed sensor data signals and processed sensor data from thesleep metering system 500 to a networked server 110 for furtherprocessing and scoring and for networked access by authorized healthcareproviders. As shown in FIGS. 32 and 33, the sleep metering system 500being worn by a subject (e.g., on-body device 500) can capture andpre-process a variety of sensor data from the sensor array of the sleepmetering system 500. For example as described above, the sleep meteringsystem 500 can detect, measure, and transmit pulse data, SpO2,respiratory effort data, breathing pattern data, airflow data, snoringdata, motion data, body position data, and other biomedical metrics andsensor data obtained from a subject wearing the sleep metering system500. This sensor data can be pre-processed by the sleep metering system500 in the manner described above. Additionally, portions of the sensordata or combinations of the sensor data can be arranged into datachannels for scoring, which can be configured for display in charts orgraphs as shown for several examples in FIGS. 34 through 39 anddescribed below.

Referring still to FIGS. 32 and 33, the sleep metering system 500 canperiodically or upon request transfer blocks of the sensor data to acentral server (cloud server) 110 via a wireless data connection andnetwork 120. As shown in FIG. 33, the sleep metering system 500 can alsosend the sensor data via a wireless hub 610, which can forward thesensor data to the central server 110 for further processing andscoring. The scoring of the sensor data by the server 110 is describedin detail above. The server 110 can also record the received sensor datato maintain a time-stamped historical record or recording of thereceived sensor data. The sensor data can also be annotated withmetadata, such as timestamps, subject identification or demographicdata, etc. The scored, recorded, and annotated sensor data for theparticular subject (e.g., the subject's sleep data) can be maintainedand stored in a cloud data storage device 103. The subject's sleep datacan be anonymized to remove specific identifying information related tothe particular subject and thereby make the retention and accessibilityof the sleep data compliant with the Health Insurance Portability andAccountability Act of 1996 (“HIPAA”). Given the implementation of asufficient level of data access security, the sleep data can be madeaccessible to authorized healthcare providers via a secure data networkconnection as shown in FIG. 32. The authorized healthcare providers canview the sleep data using a viewer and a viewing application. The viewerand viewing applications can include mobile devices, smartphones, orpersonal computers with an installed and authorized viewing application.

As shown in FIG. 33, the server 110 can also send commands,configuration data, software updates, and the like (denoted thedownstream channel) to the sleep metering system 500 via the network 120and the wireless hub 610. This downstream channel can be used tospecifically configure the operation of a particular sleep meteringsystem 500 based on specific or dynamic conditions, such as providingconfiguration parameters for a particular sleep metering system 500,configuration for a particular subject, configuration for a particularlocation or timeframe, configuration for a particular subscriptionlevel, a dynamic re-configuration a particular sleep metering system 500based on a detected set of conditions or events in the particularsubject, providing notifications, feedback, instructions, or alerts tothe subject, and the like.

FIG. 34 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a mild Obstructive Sleep Apnea (OSA) condition in the subjectbased on the detected respiration effort exaggeration event indicated bythe airflow and effort channels.

FIG. 35 illustrates an example of scoring a combined set of detectedevents and conditions in the same sleeping subject, the sample scoringindicating a mild OSA condition in the subject based on the detectedrespiration effort exaggeration event indicated by the airflow andeffort channels, wherein the events are more frequent.

FIG. 36 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating an abnormal pattern in the subject based on the detectedabdominal channel.

FIG. 37 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a severe OSA condition in the subject based on the redundantpattern detected in all sensor channels.

FIG. 38 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a moderate OSA condition in the subject based on a lack of asignificant detected snoring condition, but detection of an irregularbreathing pattern.

FIG. 39 illustrates an example of scoring a combined set of detectedevents and conditions in a sleeping subject, the sample scoringindicating a mild OSA condition in the subject based on a lack ofdetected airflow or snoring conditions, but detection of an abdominalirregular and oxygen desaturations.

FIG. 40 illustrates an example embodiment of a sleep report containing avariety of information and datasets generated using the sensor datacaptured and processed for a particular subject by the sleep meteringsystem of an example embodiment. For example, the sample sleep reportshows information datasets including: the sleep efficiency score for aparticular subject as generated by the sleep disorder diagnosis andtreatment system of an example embodiment, the particular subject'sbreathing patterns over the test period, the subject's oxygendesaturation index, BDI, a BDI severity indication, a sleep history, anda summary of the subject's sleep analysis and diagnosis. Each of theinformation datasets are based on actual sensor data retrieved from thesubject as the subject was wearing the sleep metering system 500 over atest period. As such, the information datasets generated by the sleepdisorder diagnosis and treatment system of an example embodiment arebased on the capture and processing of physical data signalsrepresenting a physical state or condition of a human subject.

The example embodiments described herein provide a technical solution toa technical problem. The various embodiments improve the functioning ofthe electronic devices and the related system by providing a system andmethod for sleep disorder diagnosis and treatment of a particular humansubject. The various embodiments also serve to transform the state ofvarious system components based on a dynamically determined systemcontext and the state of the subject being analyzed. Additionally, thevarious embodiments effect an improvement in a variety of technicalfields including the fields of dynamic data and signal processing,electronic systems, mobile devices, wearable devices, data sensingsystems, human/machine interfaces, mobile computing, informationsharing, and mobile communications.

FIGS. 41 through 44 illustrate example embodiments of the sleep meteringsystem of an example embodiment. In particular, FIGS. 41 through 44shown an example embodiment including a housing 505 containing an arrayof sensors for detecting various conditions and events in a humansubject wearing the sleep metering system 500 around their chest. Thehousing 505 is configured in an elongated low profile design to minimizethe discomfort of a subject wearing the sleep metering system 500 duringsleep. A belt or strap 510 is coupled to the housing 505 in a loop toenable the sleep metering system 500 to be attached to the chest of asubject while the subject is sleeping. In an embodiment shown in FIG.44, the housing 505 can include a removable portion 507 that can besnapped into and out of an attached portion 506. The removable portion507 enables a user to readily replace the main components of the sleepmetering system 500 to provide additional functionality, replace adefective unit, or upgrade the system.

The Sleep Efficiency Scoring Process of an Example Embodiment

As described above and in connection with the example embodimentillustrated in FIG. 19, the breathing disturbance metering system 500 ofan example embodiment can include a chest expansion measuring device 510and/or a movement detection device 520. The chest expansion measuringdevice 510 and/or the movement detection device 520 serve to detect themovements of the chest cavity during the normal breathing cycles of aperson. Once the breathing disturbance metering system 500 is calibratedto the normal breathing patterns of a particular individual, the degreeof chest cavity movement can correlate to the volume of air respired foreach breath. As explained above, anomalies in the breathing patterns ofa particular individual can correlate to variations in the degree ofchest cavity movement in a series of breaths over a given time period.In an example embodiment, the movement detection device 520 can includea level that can measure degrees of movement up and down or side toside.

These variations in breathing patterns can be aggregated into abreathing disturbance index (BDI). Respiratory effort reduction events,or RERE's, are defined, for a particular embodiment, as a >=30%reduction in amplitude of a preceding abdominal effort waveform, lasting10 seconds or longer or compared to a running baseline over apre-defined timeframe. A respiratory effort exaggeration event (REEE) isan increase in a respiratory waveform of 150% to 400% over baseline. AREEE is not counted if immediately adjacent to a RERE to avoid doublescoring the same event. A RERE that is 10-29% reduced from baseline butaccompanied by a >=3% fall in O² saturation is counted as an event. Ingeneral, the BDI for a particular subject can be determined from thegeneral equation: BDI=#RERE's+#REEE's/total recording time in hours.Segments of data artifacts are identified and removed from the equation.

In the example embodiment, the BDI can be computed as follows:

${BDI} = \frac{{\# {{RERE}'}s} + {\# {{REEE}'}s}}{\# {hours}\mspace{14mu} {of}\mspace{14mu} {{testing}/{recording}}\mspace{14mu} {time}}$

In an example embodiment, the breathing disturbance metering system 500can use clock 540 to measure a start and stop time. Initially, the usercan press a button on the device 500 to activate the device 500 whenthey retire to the bed. In the morning, the user can press a button todeactivate the device 500 after they wake up. After activation, thebreathing disturbance metering system 500 can sample the user'sbreathing patterns over pre-configured time periods (sampling period).For example, the breathing disturbance metering system 500 can beconfigured to sample the user's chest cavity movement (i.e., breathingpattern) for a five minute sampling period. During this sampling timeperiod, the breathing disturbance metering system 500 can record theuser's breathing patterns and save this data in the memory 560. At theend of the five minute sampling period, the breathing disturbancemetering system 500 can enter a low power mode (rest period) to savepower. At a configurable time later (e.g., 15 minutes), the breathingdisturbance metering system 500 can start a new five minute samplingperiod during which a new set of breathing pattern data is gathered andretained in memory 560. Each set of breathing pattern data is timestamped with the current or relative time/date. In this manner, thebreathing disturbance metering system 500 can gather breathing patterndata for the user over the entire night. As the breathing pattern datafor a particular user is gathered, the breathing disturbance meteringsystem 500 can use the processor 550 to scan the data for patternsconsistent with apnea or hypopnea conditions. As described in moredetail below, the processor 550 can score any detected respiratoryeffort reduction or exaggeration condition (e.g., RERE or REEE) for alevel of severity. The respiratory effort detection data and relatedscoring can also be stored in the memory 560. It will be apparent tothose of ordinary skill in the art that a variety of related data canalso be generated and retained. It will also be apparent to those ofordinary skill in the art that different sampling periods and/or restperiods can be used for a particular embodiment. Periodically throughoutthe night or the following morning, the breathing disturbance meteringsystem 500 can establish wireless data communications with the breathingdisturbance meter integration module 230 and upload the data saved inmemory 560 to a memory in the user platform 140. A wireless transceiverand mobile device interface 570 is provided in the breathing disturbancemetering system 500 to enable this data communication. An energy storagedevice (e.g., a battery) 580 is provided in the breathing disturbancemetering system 500 to power the system. In a particular embodiment, asound capture device (e.g., a microphone) 530 can be used to record thesounds made by the user during a sleep cycle. These sounds can includesnoring, which can also be an indicator disrupted sleep. For example, adecibel variation in a periodic manner can be consistent with sleepdisordered breathing. In this manner, the sleep disorder diagnosis andtreatment system 200 of an example embodiment can obtain real-time datarelated to the sleeping patterns of a user and data related to potentialsleep events or conditions present in a patient being monitored.

As described above, the housing 505 of the sleep metering system 500 isconfigured to include an array of sensors including: an accelerometer,gyroscope, or other motion detection device 520 to detect and measuremovement and body position of the subject wearing the sleep meteringsystem 500; and a microphone or other sound detection device to detectand measure a sound level or snoring event of the subject wearing thesleep metering system 500. The housing 505 of the sleep metering system500 is also configured with an electrical connector 507 to provide anelectrical/data interface with additional sensors of the array ofsensors. In an example embodiment, the additional sensors coupled to thehousing 505 via connector 507 can include: a pulse oximeter device 532to detect and measure a level of arterial oxygen saturation (SpO2) inthe blood and the heart rate of the subject wearing the sleep meteringsystem 500; and other biomedical devices 534 to detect and measure otherconditions and events (e.g., temperature, pressure, chemical or acidityconcentration, blood viscosity, biological magnetic field, galvanic skinresponse (GSR), wireless interfaces to implanted electromechanicaldevices, pacemakers, implanted insulin pumps, and the like) in a subjectwearing the sleep metering system 500.

Using the sleep metering system 500 and/or the sleep disorder diagnosticmodule 210 of the sleep disorder diagnosis and treatment system 200 ofan example embodiment as described herein, the example embodiment cangather raw physical sensor data associated with a subject/user andcaptured by the sleep metering system 500 while the subject/user sleeps.As described above, this raw sensor data can include data correspondingto the subject's abdominal respiratory movement over time in arespiratory waveform. The raw sensor data can also include datacorresponding to the subject's level of arterial oxygen saturation(SpO2) in the blood over time in an SpO2 waveform. The respiratorywaveform and the SpO2 waveform can be retained by the sleep meteringsystem 500 and/or the sleep disorder diagnostic module 210 in a EuropeanData Format (EDF), for example. The EDF data format is a standard fileformat designed for exchange and storage of medical time series orwaveforms. Being an open and non-proprietary format, EDF(+) is commonlyused to archive, exchange and analyze data from commercial devices in aformat that is independent of the acquisition system. Using the raw EDFdata corresponding to the subject's respiratory waveform and SpO2waveform, the sleep metering system 500 and/or the sleep disorderdiagnostic module 210 of the example embodiment can generate severalinformation datasets including: the sleep efficiency score for aparticular subject as generated by the sleep disorder diagnosis andtreatment system of an example embodiment, the particular subject'sbreathing patterns over the test period, the subject's oxygendesaturation index, a log of RERE and REEE events, BDI, a BDI severityindication, a sleep history, and a summary of the subject's sleepanalysis and diagnosis. Each of the information datasets is based onactual sensor data retrieved from the subject as the subject was wearingthe sleep metering system 500 over a test period. As such, theinformation datasets generated by the sleep disorder diagnosis andtreatment system of an example embodiment are based on the capture andprocessing of physical data signals representing a physical state orcondition of a human subject.

Referring now to FIGS. 45 through 55 in an example embodiment, a processfor generating a sleep efficiency score is described in detail. FIG. 45illustrates an example embodiment of a set of user-configurable controlvariables that can be used to configure the sleep scoring process in theexample embodiment. The sleep scoring process control variables can beconfigured by a user via a user interface presented on a display screenof a computing or communication device. In the example embodiment, theseuser-configurable control variables can include a breathing effort highbaseline, an effort low baseline, an effort quantity over baseline, anSpO2 timespan, an SpO2 percentage drop threshold, a minimum RERE time, aminimum REEE time, a merge time, a compensation gain, and a compensationoffset. The usage of these user-configurable control variables aredescribed in more detail below. The user interface can be further usedto specify whether the sleep metering system 500 should calculate thesleep efficiency score, whether to impose SpO2 absolute desaturationrequirements, and if so, an absolute desaturation threshold setpointvalue, whether to impose a minimum breathing effort threshold, and ifso, a minimum effort threshold level, and whether to ignore REEE eventswhen scoring. Each of these user-configurable control variables, alongwith the raw respiratory waveform (e.g., a first EDF file) and the rawSpO2 waveform (e.g., a second EDF file), can be used by the sleepmetering system 500 and/or the sleep disorder diagnostic module 210 togenerate the sleep efficiency score as described in detail below. Itwill be apparent to those of ordinary skill in the art in view of thedisclosure herein that the raw physical sensor data corresponding to thesubject's respiratory/abdominal effort and SpO2 levels over time can beretained and processed by the example embodiment as a single file,multiple files, or in data formats other than an EDF format.

FIG. 46 illustrates an example embodiment of the initial processing flowof the sleep scoring process used by the sleep metering system 500and/or the sleep disorder diagnostic module 210 to generate the sleepefficiency score. Initially, the raw respiratory/abdominal effortwaveform and the raw SpO2 waveform are received or obtained from thesensor data. In an example embodiment, the raw SpO2 waveform containsthe SpO2 percentage measured from the subject/user by the sleep meteringsystem 500 over time as an integer percentage (0% to 100%) sampled at arate of three Hz. It will be apparent to those of ordinary skill in theart in view of the disclosure herein that the raw SpO2 waveform can besampled at a different rate and/or resampled to a normalized rate forfurther processing. The raw respiratory/abdominal effort waveform, of anexample embodiment, contains data corresponding to the subject'srespiratory effort measured from the subject/user by the sleep meteringsystem 500 over time as an integer value (−100 to 100) sampled at a rateof 10 Hz. It will be apparent to those of ordinary skill in the art inview of the disclosure herein that the raw respiratory/abdominal effortwaveform can be sampled at a different rate and/or resampled to anormalized rate for further processing.

FIG. 47 illustrates an example embodiment of the respiratory/abdominaleffort waveform filtering and amplitude calculation of the sleep scoringprocess of the example embodiment. The resampled or normalizedrespiratory/abdominal effort waveform can be filtered using a bandpassfilter to remove outlying data. For example, a finite impulse response(FIR) filter capped at a low value of 0.16 Hz and at a high value of 0.3Hz can be used. The filtered respiratory/abdominal effort waveform canbe further processed to calculate a rolling waveform corresponding tothe raw filtered respiratory/abdominal effort waveform. The rollingwaveform is calculated based on the peaks and valleys of the filteredrespiratory/abdominal effort waveform. In an example embodiment, aquadratic least squares fit process can be used to generate the rollingwaveform. The difference between the peaks and valleys of the filteredrespiratory/abdominal effort waveform is calculated to determine therolling amplitude of the subject's respiratory effort. This rollingamplitude of the subject's respiratory effort can be retained as thesubject's current effort amplitude.

In a parallel process shown in FIG. 47, the sleep metering system 500and/or the sleep disorder diagnostic module 210 can generate thesubject's baseline effort amplitude. The rolling amplitude of thesubject's respiratory effort can be processed through a smoothing filterto produce the subject's baseline effort amplitude. The smoothing filtercan be computed as a moving average within an area over time. Thus, thesleep metering system 500 and/or the sleep disorder diagnostic module210 can generate the subject's current respiratory amplitude and thesubject's baseline respiratory effort amplitude. As described in moredetail below, these amplitudes are used as part of the sleep efficiencyscoring process of an example embodiment.

FIG. 48 illustrates an example embodiment of the SpO2 percentage dropcalculation of the sleep scoring process of the example embodiment. Thesleep metering system 500 and/or the sleep disorder diagnostic module210 can receive or obtain the subject's raw SpO2 waveform as describedabove. The raw SpO2 waveform can be used with the user-configured SpO2time control variable to obtain a portion of the raw SpO2 waveformcorresponding to the length of time specified by the user-configuredSpO2 time control variable (e.g., two minutes). This portion of the rawSpO2 waveform can be used to determine a maximum SpO2 value from theportion of the raw SpO2 waveform. This maximum SpO2 value from theuser-configured time period is used with the current SpO2 value todetermine the SpO2 percentage drop value by taking the differencebetween the subject's current SpO2 value and the subject's maximum SpO2value from the portion of the raw SpO2 waveform. This differencerepresents the subject's SpO2 percentage drop value. As described inmore detail below, the subject's SpO2 percentage drop value is used aspart of the sleep efficiency scoring process of an example embodiment.

FIG. 49 illustrates an example embodiment of the RERE scoring of thesleep scoring process of the example embodiment. As described above, thesleep metering system 500 and/or the sleep disorder diagnostic module210 can generate the subject's current respiratory amplitude and thesubject's baseline respiratory effort amplitude. The sleep meteringsystem 500 and/or the sleep disorder diagnostic module 210 can alsogenerate the subject's SpO2 percentage drop value. These computed valuesare used as part of the sleep efficiency scoring process of an exampleembodiment as shown in FIG. 49. Additionally, the sleep efficiencyscoring process can use one or more of the user-configurable controlvariables as described above. These user-configurable control variablescan include the SpO2 percentage drop threshold (e.g., default 2%), thebreathing effort high baseline (e.g., default 70%), the breathing effortlow baseline (e.g., default 50%), and the minimum RERE time (e.g.,default 7 seconds). As shown in FIG. 49, the sleep metering system 500and/or the sleep disorder diagnostic module 210 can use these values togenerate a respiratory effort reduction event score, or RERE scoring forthe subject/user. In an example embodiment, if the subject's currentrespiratory effort (current respiratory amplitude) is less than thesubject's baseline respiratory effort amplitude multiplied by thebreathing effort high baseline (e.g., default 70%) and the subject'sSpO2 percentage drop value is greater than the SpO2 percentage dropthreshold (e.g., default 2%) for a time period lasting at least theminimum RERE time (e.g., default 7 seconds), the sleep metering system500 and/or the sleep disorder diagnostic module 210 can generate,register, or score an RERE event for the subject.

In the alternative, if the subject's current respiratory effort (currentrespiratory amplitude) is less than the subject's baseline respiratoryeffort amplitude multiplied by the breathing effort low baseline (e.g.,default 50%) for a time period lasting at least the minimum RERE time(e.g., default 7 seconds), the sleep metering system 500 and/or thesleep disorder diagnostic module 210 can also generate, register, orscore an RERE event for the subject.

FIG. 50 illustrates an example embodiment of the REEE scoring of thesleep scoring process of the example embodiment. As described above, thesleep metering system 500 and/or the sleep disorder diagnostic module210 can generate the subject's current respiratory amplitude and thesubject's baseline respiratory effort amplitude. These computed valuesare used as part of the sleep efficiency scoring process of an exampleembodiment as shown in FIG. 50. Additionally, the sleep efficiencyscoring process can use one or more of the user-configurable controlvariables as described above. These user-configurable control variablescan include the effort quantity over baseline (e.g., default 250%) and aminimum REEE time (e.g., default 7 seconds). As shown in FIG. 50, thesleep metering system 500 and/or the sleep disorder diagnostic module210 can use these values to generate a respiratory effort exaggerationevent score, or REEE scoring for the subject/user. In an exampleembodiment, if the subject's current respiratory effort (currentrespiratory amplitude) is greater than the subject's baselinerespiratory effort amplitude multiplied by the effort quantity overbaseline value (e.g., default 250%) for a time period lasting at leastthe minimum REEE time (e.g., default 7 seconds), the sleep meteringsystem 500 and/or the sleep disorder diagnostic module 210 can generate,register, or score an REEE event for the subject.

FIG. 51 illustrates an example embodiment of the event coalescingprocessing of the sleep scoring process of the example embodiment. Insome circumstances, a subject's RERE and REEE events, as scored in themanner described above, can occur in bursts, clusters, or in rapidsuccession. In these cases, it is better to consolidate these RERE andREEE event clusters into single RERE and REEE events. The eventcoalescing processing of the sleep scoring process performs thisconsolidation function as shown in FIG. 51. As shown, the sleep meteringsystem 500 and/or the sleep disorder diagnostic module 210 can detect anarray or cluster or similar events (e.g., all RERE events or all REEEevents) over a configurable time period. The time period can bespecified using a user-configurable control variable (e.g., merge time,default 7 seconds). If the sleep metering system 500 and/or the sleepdisorder diagnostic module 210 detects an array or cluster of similarevents (e.g., all RERE events or all REEE events) within theconfigurable merge time (e.g., default 7 seconds), the sleep scoringprocess will replace the cluster of similar events (e.g., all REREevents or all REEE events) with a corresponding single event. In thismanner, the subject's overall sleep efficiency scoring more accuratelyreflects the subject's actual nightly sleep condition.

FIG. 52 illustrates an example embodiment of the breathing disturbanceindex (BDI) calculation of the sleep scoring process of the exampleembodiment. In general, the BDI is calculated as the number of REREevents and REEE events (sleep events), as computed in the mannerdescribed above, divided by the subject's sleep duration in hours. Inother words, the BDI is a ratio of the number of sleep events over thesleep duration. While the BDI is a useful metric related to thesubject's sleep efficiency, another useful metric is a compensated BDIscore calculated by the sleep metering system 500 and/or the sleepdisorder diagnostic module 210 from the BDI and user-configured controlvariables: compensation gain (e.g., default 0.4 seconds) andcompensation offset (e.g., default 15). The compensated BDI score isused to compensate for the fact that some level of sleep events occureven in a healthy and efficient sleep cycle. Moreover, as describedabove, clusters of sleep events can also occur in a normal sleep cycle.The compensated BDI score adjusts the BDI based on a user-configuredcompensation gain and compensation offset. In an example embodiment asshown in FIG. 52, the compensated BDI score is calculated as the sum ofthe BDI and the product of the compensation gain and the differencebetween the BDI and the compensation offset. The compensated BDI scoreis another useful metric related to the subject's sleep efficiency.

FIGS. 53 and 54 illustrate an example embodiment of the EDF annotationsfile and log files created by the sleep scoring process of the exampleembodiment. Once the sleep metering system 500 and/or the sleep disorderdiagnostic module 210 compile the subject's sleep event and sleepefficiency scoring data and metrics as described above, the subject'ssleep data and metrics can be used to create one or more EDF annotationsfiles and log files. In particular, the subject's respiratory/abdominaleffort waveform and the SpO2 waveform can be captured and retained in anEDF file. Additionally, the EDF file can be annotated to include thedetected sleep event types, sleep event start times, and sleep eventdurations for each of the sleep events as detected by the sleep meteringsystem 500 and/or the sleep disorder diagnostic module 210 in the mannerdescribed above. The EDF file can be viewed using a standard EDF dataformat viewer. FIG. 53 illustrates an example of the annotation dataproduced by the sleep metering system 500 and/or the sleep disorderdiagnostic module 210 of an example embodiment. As shown, the annotationdata can include a plurality of data entries corresponding to thedetected sleep event types, sleep event start times, and sleep eventdurations that occurred during a subject's sleep cycle.

Additionally, as shown in FIG. 54, the EDF annotations files and logfiles can include a total of the number of sleep events detected, thetime period of the subject's sleep cycle, the number of sleep events pertime period (e.g., one hour), and a recordation of the user-configuredcontrol variables used by the sleep efficiency scoring process for thesubject's sleep cycle. The example embodiment can also generate a plotof the sleep events that occurred during the subject's sleep cycle.

FIG. 55 is a processing flow chart illustrating an example embodiment ofa method 1100 as described herein. The method 1100 of the exampleembodiment includes: establishing a wireless data communicationinterface between a networked server and a sleep metering system worn bya user, the sleep metering system including a sensor array, wirelesstransceiver, and a processor (operation 1110); activating the sleepmetering system to begin collection of sensor data from the user basedon data signals from the sensor array of the sleep metering system(operation 1120); receiving a respiratory waveform and datacorresponding to a level of arterial oxygen saturation (SpO2) in theuser's blood over time as an SpO2 waveform based on the collected sensordata (operation 1130); receiving a set of user-configured controlvariables (operation 1140); and generating a sleep efficiency scorebased on the respiratory waveform, the SpO2 waveform, and theuser-configured control variables, the sleep efficiency score includinga log of the user's respiratory effort reduction events (RERE) andrespiratory effort exaggeration events (REEE) (operation 1150).

The various elements of the example embodiments as previously describedwith reference to the figures may include various hardware elements,software elements, or a combination of both. Examples of hardwareelements may include devices, logic devices, components, processors,microprocessors, circuits, processors, circuit elements (e.g.,transistors, resistors, capacitors, inductors, and so forth), integratedcircuits, application specific integrated circuits (ASIC), programmablelogic devices (PLD), digital signal processors (DSP), field programmablegate array (FPGA), memory units, logic gates, registers, semiconductordevice, chips, microchips, chip sets, and so forth. Examples of softwareelements may include software components, programs, applications,computer programs, application programs, system programs, softwaredevelopment programs, machine programs, operating system software,middleware, firmware, software modules, routines, subroutines,functions, methods, procedures, software interfaces, application programinterfaces (API), instruction sets, computing code, computer code, codesegments, computer code segments, words, values, symbols, or anycombination thereof. However, determining whether an embodiment isimplemented using hardware elements and/or software elements may vary inaccordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a givenimplementation.

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in a single embodiment for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment.

1.20. (canceled)
 21. A computer-implemented method for improvingdetection of sleep-related conditions, the method comprising:establishing, by use of a data processor, a wireless data communicationinterface between the data processor and a sleep metering system worn bya user, the sleep metering system including a sensor array, wirelesstransceiver, and a processor; positioning the sleep metering system fordata recording externally and proximate to the user's abdomen to detectand measure airflow and respiratory effort in the user; collecting, byuse of the sleep metering system, sensor data from the user based ondata signals from the sensor array of the sleep metering system;receiving, by use of the data processor, an abdominal effort waveformbased on the collected sensor data from the sleep metering system, theabdominal effort waveform representing breathing pattern datacorresponding to variations in the degree of the user's abdominal effortin a series of breaths over a given time interval; retrieving a set ofuser-configured control variables; identifying, by use of the dataprocessor, the user's respiratory effort reduction events (RERE) and theuser's respiratory effort exaggeration events (REEE) using the breathingpattern data from the sleep metering system and the user-configuredcontrol variables; and anonymizing the user's collected sensor data toremove specific identifying information related to the particular user.22. The computer-implemented method of claim 21 wherein identifying theuser's RERE REEE includes filtering the breathing pattern data.
 23. Thecomputer-implemented method of claim 21 including generating a sleepefficiency score.
 24. The computer-implemented method of claim 23wherein generating the sleep efficiency score includes scoring an REREevent based on the user-configured control variables.
 25. Thecomputer-implemented method of claim 23 wherein generating the sleepefficiency score includes scoring an RERE event based on whether acurrent respiratory effort value from the breathing pattern data exceedsa baseline value from the user-configured control variables.
 26. Thecomputer-implemented method of claim 23 wherein generating the sleepefficiency score includes scoring an REEE event based on whether acurrent respiratory effort value from the breathing pattern data exceedsa baseline value from the user-configured control variables.
 27. Thecomputer-implemented method of claim 23 wherein generating the sleepefficiency score includes coalescing a plurality of sleep events into asingle sleep event.
 28. The computer-implemented method of claim 23wherein generating the sleep efficiency score includes generating abreathing disturbance index (BDI) score based on a number of detectedsleep events, and generating a compensated breathing disturbance index(BDI) score based on the BDI score and values from the user-configuredcontrol variables.
 29. The computer-implemented method of claim 21including generating an annotated log file including entriescorresponding to detected sleep events.
 30. The computer-implementedmethod of claim 21 including generating a sleep report containinginformation and datasets generated using the sensor data captured andprocessed for the user.
 31. A system for improving detection ofsleep-related conditions, the system comprising: a data processor; amemory, in data communication with the data processor; a sleep meteringsystem in wireless data communication with the data processor, the sleepmetering system being worn by a user and including a sensor array,wireless transceiver, and a processor, the sleep metering system beingpositioned for data recording externally and proximate to the user'sabdomen to detect and measure airflow and respiratory effort in theuser; and a sleep disorder diagnosis and treatment system, executable bythe data processor, to: establish a wireless data communicationinterface between the data processor and the sleep metering system;activate the sleep metering system to collect sensor data from the userbased on data signals from the sensor array of the sleep meteringsystem; receive from the sleep metering system an abdominal effortwaveform based on the collected sensor data from the sleep meteringsystem, the single abdominal effort channel waveform representingbreathing pattern data corresponding to variations in the degree of theuser's abdominal effort in a series of breaths over a given timeinterval; retrieve a set of user-configured control variables; identifythe user's respiratory effort reduction events (RERE) and the user'srespiratory effort exaggeration events (REEE) using the breathingpattern data from the sleep metering system and the user-configuredcontrol variables; and anonymize the user's collected sensor data toremove specific identifying information related to the particular user.32. The system of claim 31 wherein the sleep disorder diagnosis andtreatment system being further configured to filter the breathingpattern data.
 33. The system of claim 31 wherein the sleep disorderdiagnosis and treatment system being further configured to generate asleep efficiency score.
 34. The system of claim 33 wherein the sleepdisorder diagnosis and treatment system being further configured toscore an RERE event based on the user-configured control variables. 35.The system of claim 33 wherein the sleep disorder diagnosis andtreatment system being further configured to score an RERE event basedon whether a current respiratory effort value from the breathing patterndata exceeds a baseline value from the user-configured controlvariables.
 36. The system of claim 33 wherein the sleep disorderdiagnosis and treatment system being further configured to score an REEEevent based on whether a current respiratory effort value from thebreathing pattern data exceeds a baseline value from the user-configuredcontrol variables.
 37. The system of claim 33 wherein the sleep disorderdiagnosis and treatment system being further configured to coalesce aplurality of sleep events into a single sleep event.
 38. The system ofclaim 33 wherein the sleep disorder diagnosis and treatment system beingfurther configured to generate a breathing disturbance index (BDI) scorebased on a number of detected sleep events, and to generate acompensated breathing disturbance index (BDI) score based on the BDIscore and values from the user-configured control variables.
 39. Thesystem of claim 31 wherein the sleep disorder diagnosis and treatmentsystem being further configured to generate an annotated log fileincluding entries corresponding to detected sleep events.
 40. The systemof claim 31 wherein the sensor array includes sensors of a type from thegroup consisting of: a motion detection device, a sound detectiondevice, a pulse oximeter device, and a respiratory effort detectiondevice.